2 citations · 2 across the 1 of their papers we have counts for
4 papers
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Amanda Bower, Kristian Lum, Tomo Lazovich +2
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic o…
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Rebecca L. Russell, Louis Kim, Lei H. Hamilton +5
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can…
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Jacob Harer, Onur Ozdemir, Tomo Lazovich +4
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target do…
Automated software vulnerability detection with machine learning
Jacob A. Harer, Louis Y. Kim, Rebecca L. Russell +13
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered i…